A Comprehensive Review on Applications, Techniques and Frameworks of Deep Learning

Authors

  • K. Mary Sudha Rani  Assistant Professor, Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, India
  • G. Satvik Kalyan  Student, B.E, Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, India

Keywords:

Deep Learning, models, applications

Abstract

Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. Deep learning is a set of learning methods attempting to model data with complex architectures combining different non-linear transformations. The elementary bricks of deep learning are the neural networks, that are combined to form the deep neural networks. This paper gives a detailed review on applications, techniques and frameworks of deep learning.

References

  1. B.T. Polyak. Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5):1–17, 1964.
  2. Hochreiter S. and SchmidhuberJ. Long short-term memory. Neural Com- putation, 9(8):1735–1780, 1997.
  3. I. Sutskever, J. Martens, G.E. Dahl, and G.E. Hinton. On the importance of initialization and momentum in deep learning. ICML, 28(3):1139– 1147, 2013.
  4. ANUMANDLA MOUNIKA, “A STUDY ON CLOUD COMPUTING STRATEGY PLANNING AND SLA MANAGEMENT IN CLOUD”, International Journal of Research, Volume 7, Issue VII, JULY 2018
  5. ANUMANDLA MOUNIKA, “A REVIEW ON CLOUD COMPUTING PLATFORMS AND ENTERPRISE CLOUD COMPUTING PARADIGM”, The International journal of analytical and experimental modal analysis, Volume III, Issue II, July-November-2011
  6. Bhagya Rekha Kalukurthi, “SOLVING MULTIPLE OPTIMIZATION PROBLEMS USING HADOOP AND THE ROLE OF BIG DATA ANALYTICS IN OPTICAL NETWORKS”, The International journal of analytical and experimental modal analysis, Volume XIII, Issue I, January 2021
  7. BhagyaRekha Kalukurthi, “Data Mining Strategy for Discovering Intriguing Patterns and Challenges with Bigdata for Global Pulse Development”, International Journal of Scientific Research in Science and Technology, Volume 3, Issue 3, March-April 2017
  8. Malyadri. K, “Architecture and Components of Cloud-Based ML Framework”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 8, Issue 1, January 2019
  9. Malyadri. K, “An Overview towards the Different Types of Security Attacks", International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 8, August 2014
  10. Malyadri. K, “Security Threats, Security Vulnerabilities and Advance Network Security Policies”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, Issue 9, September 2013
  11. Malyadri. K, “Need for Key Management in Cloud and Comparison of Various Encryption Algorithm”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology , volume 1, issue 1, July-August 2016
  12. Malyadri. K, “Integration of Appropriate Analytic tools towards Mobile Technology Development”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 6, Issue 6, June 2018
  13. Malyadri. K,“A STUDY ON EXPERIENCES ANDLIMITATIONS OF MOBILE COMMUNICATION”, Alochana Chakra Journal, Volume VI, Issue VIII, August 2017
  14. Malyadri. K,“CHALLENGES CONCERNING MOBILEDEVELOPMENT AND MODEL-DRIVEN DEVELOPMENT OF MOBILE APPS”, Airo International Research Journal, volume XVI, Nov 2018
  15. Malyadri, N. Surya Teja, “Related technologies and the role of mobile app development life cycle”, International Journal of Research and Applications, Volume 5, Issue 17, Jan-Mar 2018.
  16. Rakesh Rojanala, “Cloud Computing Characteristics and Deployment of Big Data Analytics in The Cloud”, International Journal of Scientific Research in Science and Technology, Volume VIII, Issue II, March-April 2014
  17. Rakesh Rojanala, “CLOUD COMPUTING ARCHITECTURAL FRAMEWORK”, Journal of Interdisciplinary Cycle Research, Volume V, Issue I, Jan- June 2013
  18. Rakesh Rojanala, “A COMPREHENSIVE STUDY ON THECHALLENGES OF STREAM DATA MINING AND BIG DATA-ORIENTED STREAM DATA MINING”, The International journal of analytical and experimental modal analysis, Volume VII, Issue II, July-December 2015
  19. Rakesh Rojanala, “Machine Learning: Intersection of Statistics and Computer Science”, “International Journal of Innovative Research in Computer and Communication Engineering”, Vol. 5, Issue 8, August 2017
  20. LeCun Y., Bottou L., Bengio Y., and Haffner P.  Gradient-based learn- ing applied to document recognition. IEEE Communications magazine, 27(11):41–46, 1998.
  21. Wei-Yang Lin, Ya-Han Hu Chih-Fong Tsai, “Machine Learning in Financial Crisis Prediction: A Survey,” IEEE Trans. On System, Man and Cybernatics—Part C: Application and Reviews, Vol. 42, No. 4, July 2012.
  22. Mario Bkassiny Yang Li, Sudharman K. Jayaweera, “A Survey on Machine-Learning Techniques in Cognitive Radios,” IEEE Communications Surveys & Tutorials, Vol. 15, No. 3, Third Quarter 2013.
  23. Li Deng, Xiao Li, “Machine Learning Paradigms for Speech Recognition: An Overview,” IEEE Trans On Audio, Speech, And Language Processing, Vol. 21, NO. 5, May 2013.
  24. Daizhan Cheng, Hongsheng Qi, “State–Space Analysis of Boolean Networks,” IEEE Trans.On Neural Networks, Vol. 21, No. 4, April 2010.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2019-12-30

Issue

Volume 6, Issue 6, November-December-2019

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Mary Sudha Rani, G. Satvik Kalyan, " A Comprehensive Review on Applications, Techniques and Frameworks of Deep Learning, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 6, Issue 6, pp.388-394, November-December-2019.